Integrated Commodity Flow Survey with Advanced Technology Moshe Ben-Akiva August 2015 Workshop FHWA – new CFS national-wide

Outline Future Mobility Sensing Truckers @ MIT Integrated approach

Future Mobility Sensing Truckers @ MIT Integrated approach

July 2, 2015 | Presentation to MoT Future Mobility Sensing Automated travel survey that leverages increasingly pervasive smartphone ownership advanced sensing technologies machine learning techniques to deliver previously unobtainable range of behavioral data and insights. July 2, 2015 | Presentation to MoT

Automated and integrated travel survey system

User Interfaces Non-intrusive iOS and Android apps User friendly activity diary that users can edit and provide additional information

Field Test in Singapore LTA conducted Household Interview Travel Survey (HITS) 2012 with ~10,000 households. More than 1500 HITS respondents also participated in FMS demonstration project (October 2012 – September 2013) Known issues in traditional method: Short activities under-reported Over-estimated travel times for short trips Reporting of a simple (typical) day FMS delivers richer, higher resolution, multi-day travel and activity dataset

HITS vs FMS: An example

Recent Developments Enhanced technology Additional capabilities Event based on-phone surveys Happiness Transit quality Context specific SP Commercialization

Future Mobility Sensing Truckers @ MIT Integrated approach

Motivation Toll roads (Perez and Lockwood 2006) 30-40% of new urban expressway mileage in the US 150 new centerline miles expected per year Heavy trucks on typical toll road (S&P 2005) 10% of traffic flow 25% of revenue Toll road forecasts biased and with high variance (Bain 2009)

This study survey of truck route choice data collected directly from drivers two phases: Phase I – Driver questionnaires with route choice stated preferences (SP) Phase II – GPS-based revealed preferences (RP) data

SP study: Effect of tolls

Phase I – Key findings Wide variability in preferences towards toll roads and tolls Route choices depend on multiple factors Travel time, tolls, delays Toll bearing terms Driver compensation method Shipment characteristics For more details: Moshe Ben Akiva, Hilde Meersman, Eddy van de Voorde (eds), Freight Transport Modelling, Emerald Books, May 2013

Phase II – RP data collection (adaptation of FMS) Remove battery level

GPS logger Trucks equipped with off-the-shelf loggers (SANAV CT-24) Monitor all trips continuously Transmit data in real-time to server Collects: Location data Speed Timestamp Report Intervals Time intervals Minimum distance

Backend Algorithms Applied to the data received by the backend (MIT server): Trace creation (FMS) Stop detection (FMS) Map Matching (Open Street Map) Toll detection (Open Street Map)

Web interface Validate and correct movement information Collect additional information Pick-up & delivery schedules Cargo type Tolls, methods of paying Exit survey Personal information Context specific SP

Web interface

Exit Survey

Data collection process Over the phone using lists of trucking companies At truck stops and rest areas Indiana Massachusetts Texas Ontario

Driver type: Long tour

Driver type: Short tour

Driver type: ‘Gypsy’

Same driver, different route This is a map of the Austin area, which has two north-south routes passing through it – I-35 (free) and SH-130 (tolled). This driver went south from Dallas to San Antonio in the morning peak, using SH-130.

Same driver, different route Two days later, he came back on a Saturday morning via the free road, I-35.

Same day, different route This is a similar example from the Chicago area. The driver is based in the southeastern corner of the map, went up to Milwaukee via the Indiana Toll Road and Chicago Skyway, then returned the same day via a different toll road, I-294 (to the west of Chicago).

Truck Drivers’ Survey in Singapore System setup for data collection in Singapore New questionnaires designed for urban freight

Truck Telematics - OBD Devices Use the On-Board Diagnostic (OBD) port to connect to vehicle’s engine Data collected (second-by-second): GPS location vehicle speed fuel consumption other engine parameters (engine rpm, air intake temperature, etc.) Able to track route, stops, driver behavior, idling, fuel use and emissions

Sample OBD Data from a Truck Single trip sample OBD data logged Logged truck trips in a single day Idling as % of trip time = 51% Idling as % of fuel use = 25%

Future Mobility Sensing Truckers @ MIT Integrated approach

Integrated approach integrated survey design establishments carriers/drivers innovative technology FMS tracking/tracing of vehicles and shipments urban CFS and nationwide CFS

Integrated approach (cont’d) Business Establishments Tablet-based questionnaire Needs and capacity, storage, parking, loading and unloading, fleet size, etc. Commodities Tracking shipment RFID tags attached to shipments Truck Drivers GPS logger Web-based or tablet-based verification Carriers Web-based questionnaire and GPS loggers for drivers

Operational flow Establishment and Driver survey 2a. Producer Questionnaire Tag shipments 3. Truck driver Pick-up/delivery 1. Surveyor 2b. Retailer, etc. Questionnaire Tag shipments 5. Truck driver (hired or owned) Verify stop purpose and commodity type 4. Carriers Web-based questionnaire Carrier and Driver survey

Integrated technology

Establishments and carrier surveys Tablet-based questionnaires and shipment tracking TRACKING SHIPMENTS WEB- TABLET- BASED SURVEYS GIS data & POI Raw data Survey Data Server

Thank You! mba@mit.edu